Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
This addresses the low-resource challenge for Chinese-Portuguese machine translation, benefiting researchers and developers in this language pair, though it is incremental as it applies existing methods to new data.
The paper tackled the lack of parallel corpora for Chinese-Portuguese machine translation by building a large parallel corpus from Macao government websites, resulting in a benchmark for future systems with improved translation performance using PBMT and NMT models.
Although there are increasing and significant ties between China and Portuguese-speaking countries, there is not much parallel corpora in the Chinese-Portuguese language pair. Both languages are very populous, with 1.2 billion native Chinese speakers and 279 million native Portuguese speakers, the language pair, however, could be considered as low-resource in terms of available parallel corpora. In this paper, we describe our methods to curate Chinese-Portuguese parallel corpora and evaluate their quality. We extracted bilingual data from Macao government websites and proposed a hierarchical strategy to build a large parallel corpus. Experiments are conducted on existing and our corpora using both Phrased-Based Machine Translation (PBMT) and the state-of-the-art Neural Machine Translation (NMT) models. The results of this work can be used as a benchmark for future Chinese-Portuguese MT systems. The approach we used in this paper also shows a good example on how to boost performance of MT systems for low-resource language pairs.